{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import gzip\n",
    "import ujson as json\n",
    "from itertools import islice, takewhile\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "from collections import Counter, defaultdict\n",
    "import random\n",
    "import json\n",
    "import numpy\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'mimo'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-57dcda612695>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmimo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0miter_instance_decodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_decodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_summary_metrics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m: No module named 'mimo'"
     ]
    }
   ],
   "source": [
    "from mimo.evaluate import iter_instance_decodes, evaluate_decodes, get_summary_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = '../data/results/mentions/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "relation_name_map = {\n",
    "    'country of citizenship': 'citizenship',\n",
    "    'member of sports team': 'sports team',\n",
    "    'member of political party': 'political party'\n",
    "}\n",
    "\n",
    "relation_order = ['<'+r.replace(' ', '_')+'>' for r in [\n",
    "    'sex or gender',\n",
    "    'date of birth',\n",
    "    'occupation',\n",
    "    'given name',\n",
    "    'country of citizenship',\n",
    "    'place of birth',\n",
    "    'date of death',\n",
    "    'place of death',\n",
    "    'educated at',\n",
    "    'sport',\n",
    "    'member of sports team',\n",
    "    'position held',\n",
    "    'award received',\n",
    "    'family name',\n",
    "    'participant of',\n",
    "    'member of political party',\n",
    "]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c916bb2479dd4702866d82a0f49a0c8f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
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       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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       "</p>\n"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def iter_entity_decodes(path):\n",
    "    with gzip.open(path, 'rt', encoding='utf-8') as f:\n",
    "        for line in tqdm(f):\n",
    "            yield json.loads(line)['decode']\n",
    "\n",
    "metrics = evaluate_decodes(iter_entity_decodes(os.path.join(data_path, 'output.jsonl.gz')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\hline\\hline\n",
      "   \\texttt{sex or gender} & 139,272 & 83.5 & 94.2 & 99.0 \\\\\n",
      "   \\texttt{date of birth} & 118,414 &  0.2 & 75.4 & 80.5 \\\\\n",
      "      \\texttt{occupation} & 111,462 & 11.8 & 69.8 & 88.1 \\\\\n",
      "      \\texttt{given name} & 110,770 &  3.4 & 88.0 & 94.1 \\\\\n",
      "     \\texttt{citizenship} & 102,246 & 28.1 & 89.2 & 94.7 \\\\\n",
      "  \\texttt{place of birth} & 81,324 &  1.5 & 25.7 & 36.9 \\\\\n",
      "   \\texttt{date of death} & 55,610 &  0.1 & 68.3 & 75.4 \\\\\n",
      "  \\texttt{place of death} & 27,618 &  3.8 & 27.8 & 39.2 \\\\\n",
      "     \\texttt{educated at} & 25,633 &  3.7 & 16.3 & 33.0 \\\\\n",
      "           \\texttt{sport} & 23,067 & 56.9 & 87.1 & 98.1 \\\\\n",
      "     \\texttt{sports team} & 21,841 &  0.5 & 17.0 & 31.3 \\\\\n",
      "   \\texttt{position held} & 13,953 &  6.3 & 63.0 & 78.8 \\\\\n",
      "  \\texttt{award received} & 12,196 &  4.6 & 38.8 & 56.6 \\\\\n",
      "     \\texttt{family name} & 11,368 &  4.4 & 61.5 & 70.4 \\\\\n",
      "  \\texttt{participant of} & 11,054 &  6.3 & 44.5 & 81.1 \\\\\n",
      " \\texttt{political party} & 10,409 & 18.3 & 60.6 & 83.8 \\\\\n",
      "\\hline\\hline\n",
      "\\multicolumn{2}{r}{\\texttt{Micro Avg.}} & 20.9 & 70.0 & 79.5 \\\\\n",
      "\\multicolumn{2}{r}{\\texttt{Macro Avg.}} & 14.6 & 58.0 & 71.3 \\\\\n",
      "\\hline\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "import numpy\n",
    "\n",
    "\n",
    "def print_latex_evaluation(metrics):\n",
    "    systems = ['base', 'sys@1', 'sys@5']\n",
    "    micro_scores = defaultdict(list)\n",
    "    macro_scores = defaultdict(list)\n",
    "    print('\\\\hline\\\\hline')\n",
    "    for r, system_scores in sorted(metrics.items(), key=lambda kv: relation_order.index(kv[0])):\n",
    "        r_fmt = r.replace('<', '').replace('>', '').replace('_', ' ')\n",
    "        r_fmt = relation_name_map.get(r_fmt, r_fmt)\n",
    "        r_fmt = '\\\\texttt{' + r_fmt + '}'\n",
    "        \n",
    "        report = r_fmt.rjust(25)\n",
    "        \n",
    "        N = len(system_scores[systems[0]])\n",
    "        report += ' & ' + \"{:,}\".format(N).rjust(6)\n",
    "\n",
    "        for s, scores in [(s, system_scores[s]) for s in systems]:\n",
    "            score = numpy.mean(scores)\n",
    "            micro_scores[s].extend(scores)\n",
    "            macro_scores[s].append(score)\n",
    "            report += ' & ' + ('%.1f' % (100 * score)).rjust(4)\n",
    "        print(report + ' \\\\\\\\')\n",
    "    print('\\\\hline\\\\hline')\n",
    "    print('\\\\multicolumn{2}{r}{\\\\texttt{Micro Avg.}}'.rjust(25+9) + ' & ' + ' & '.join(('%.1f' % (100 * numpy.mean(micro_scores[s]))).ljust(4) for s in systems) + ' \\\\\\\\')\n",
    "    print('\\\\multicolumn{2}{r}{\\\\texttt{Macro Avg.}}'.rjust(25+9) + ' & ' + ' & '.join(('%.1f' % (100 * numpy.mean(macro_scores[s]))).ljust(4) for s in systems) + ' \\\\\\\\')\n",
    "    print('\\\\hline')\n",
    "print_latex_evaluation(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspecting model output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "6dc7c9d61c9b42b489b9f3ce03c7dd9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
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       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "instances = {}\n",
    "with gzip.open(os.path.join(data_path, 'output.jsonl.gz'), 'rt', encoding='utf-8') as f:\n",
    "    for line in tqdm(islice(f, 5000)):\n",
    "        obj = json.loads(line)\n",
    "        instances[obj['instance_id']] = obj['decode']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"sources\": [\n",
      "    \"his highest score came in 1939 when he made an innings of 217 against northumberland , he shared in a minor counties record first-wicket partnership of 323 with | harold theobald | .\"\n",
      "  ],\n",
      "  \"targets\": {\n",
      "    \"<date_of_death>\": \"1982 07 20\",\n",
      "    \"<date_of_birth>\": \"1896 03 18\",\n",
      "    \"<place_of_birth>\": \"norwich\",\n",
      "    \"<sex_or_gender>\": \"male\",\n",
      "    \"<country_of_citizenship>\": \"united kingdom\",\n",
      "    \"<place_of_death>\": \"norwich\",\n",
      "    \"<given_name>\": \"harold\",\n",
      "    \"<occupation>\": \"cricketer\"\n",
      "  },\n",
      "  \"decodes\": {\n",
      "    \"<given_name>\": [\n",
      "      {\n",
      "        \"score\": -0.0072908401,\n",
      "        \"tokens\": [\n",
      "          \"harold\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"harold\"\n",
      "      }\n",
      "    ],\n",
      "    \"<family_name>\": [\n",
      "      {\n",
      "        \"score\": -0.0005378723,\n",
      "        \"tokens\": [\n",
      "          \"<unk>\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"<unk>\"\n",
      "      }\n",
      "    ],\n",
      "    \"<sex_or_gender>\": [\n",
      "      {\n",
      "        \"score\": 0.0,\n",
      "        \"tokens\": [\n",
      "          \"male\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"male\"\n",
      "      }\n",
      "    ],\n",
      "    \"<date_of_birth>\": [\n",
      "      {\n",
      "        \"score\": -3.923664093,\n",
      "        \"tokens\": [\n",
      "          \"1912\",\n",
      "          \"01\",\n",
      "          \"01\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"1912 01 01\"\n",
      "      }\n",
      "    ],\n",
      "    \"<occupation>\": [\n",
      "      {\n",
      "        \"score\": -0.0610628128,\n",
      "        \"tokens\": [\n",
      "          \"cricketer\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"cricketer\"\n",
      "      }\n",
      "    ],\n",
      "    \"<country_of_citizenship>\": [\n",
      "      {\n",
      "        \"score\": -0.3711585999,\n",
      "        \"tokens\": [\n",
      "          \"united\",\n",
      "          \"kingdom\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"united kingdom\"\n",
      "      }\n",
      "    ],\n",
      "    \"<sport>\": [\n",
      "      {\n",
      "        \"score\": -0.1953792572,\n",
      "        \"tokens\": [\n",
      "          \"athletics\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"athletics\"\n",
      "      }\n",
      "    ],\n",
      "    \"<date_of_death>\": [\n",
      "      {\n",
      "        \"score\": -4.4913721085,\n",
      "        \"tokens\": [\n",
      "          \"1960\",\n",
      "          \"01\",\n",
      "          \"01\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"1960 01 01\"\n",
      "      }\n",
      "    ],\n",
      "    \"<place_of_birth>\": [\n",
      "      {\n",
      "        \"score\": -4.8144302368,\n",
      "        \"tokens\": [\n",
      "          \"chicago\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"chicago\"\n",
      "      }\n",
      "    ],\n",
      "    \"<educated_at>\": [\n",
      "      {\n",
      "        \"score\": -2.8010969162,\n",
      "        \"tokens\": [\n",
      "          \"eton\",\n",
      "          \"college\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"eton college\"\n",
      "      }\n",
      "    ],\n",
      "    \"<member_of_sports_team>\": [\n",
      "      {\n",
      "        \"score\": -3.5370111465,\n",
      "        \"tokens\": [\n",
      "          \"kolkata\",\n",
      "          \"knight\",\n",
      "          \"riders\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"kolkata knight riders\"\n",
      "      }\n",
      "    ],\n",
      "    \"<place_of_death>\": [\n",
      "      {\n",
      "        \"score\": -2.4424953461,\n",
      "        \"tokens\": [\n",
      "          \"london\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"london\"\n",
      "      }\n",
      "    ],\n",
      "    \"<position_held>\": [\n",
      "      {\n",
      "        \"score\": -0.9836673737,\n",
      "        \"tokens\": [\n",
      "          \"member\",\n",
      "          \"of\",\n",
      "          \"parliament\",\n",
      "          \"in\",\n",
      "          \"the\",\n",
      "          \"united\",\n",
      "          \"kingdom\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"member of parliament in the united kingdom\"\n",
      "      }\n",
      "    ],\n",
      "    \"<participant_of>\": [\n",
      "      {\n",
      "        \"score\": -0.7339658737,\n",
      "        \"tokens\": [\n",
      "          \"1948\",\n",
      "          \"summer\",\n",
      "          \"olympics\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"1948 summer olympics\"\n",
      "      }\n",
      "    ],\n",
      "    \"<member_of_political_party>\": [\n",
      "      {\n",
      "        \"score\": -0.8230638504,\n",
      "        \"tokens\": [\n",
      "          \"republican\",\n",
      "          \"party\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"republican party\"\n",
      "      }\n",
      "    ],\n",
      "    \"<award_received>\": [\n",
      "      {\n",
      "        \"score\": -0.4960975647,\n",
      "        \"tokens\": [\n",
      "          \"wisden\",\n",
      "          \"cricketer\",\n",
      "          \"of\",\n",
      "          \"the\",\n",
      "          \"year\",\n",
      "          \"</s>\"\n",
      "        ],\n",
      "        \"source_id\": 0,\n",
      "        \"decoded\": \"wisden cricketer of the year\"\n",
      "      }\n",
      "    ]\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "instance = next(iter(instances.values()))\n",
    "instance = dict(instance)\n",
    "instance['decodes'] = {r: v[:1] for r, v in instance['decodes'].items()}\n",
    "print(json.dumps(instance, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sampling Generated Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\\small in a minor counties record first-wicket partnership of 323 with | harold theobald | .}\n"
     ]
    }
   ],
   "source": [
    "def format_source(source):\n",
    "    if source.count('|') != 2 or len(source) < 30:\n",
    "        return None\n",
    "\n",
    "    tokens = source.split()\n",
    "    span_left = tokens.index('|')\n",
    "    span_right = len(tokens) - tokens[::-1].index('|')\n",
    "    span = tokens[span_left:span_right]\n",
    "    left = tokens[:span_left]\n",
    "    right = tokens[span_right:]\n",
    "    \n",
    "    max_window_sz = 10\n",
    "    \n",
    "    output = '{\\\\small ' + ' '.join(left[-max_window_sz:]) + ' '\n",
    "    output += ''+ ' '.join(span) + ' '\n",
    "    output += '' + ' '.join(right[:max_window_sz]) + '}'\n",
    "\n",
    "    return output\n",
    "\n",
    "print(format_source(instance['sources'][0]))\n",
    "#for left, span, right in random.sample(elon['mentions'], 5):\n",
    "#    print '{\\\\small ...', ' '.join(left[-5:]), '} &', '\\\\textttt{'+ ' '.join(span) + '}', '& {\\\\small', ' '.join(right[:5]), '...} \\\\\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rels = [\n",
    "    '<given_name>',\n",
    "    '<occupation>',\n",
    "    '<country_of_citizenship>',\n",
    "    '<sex_or_gender>',\n",
    "    '<date_of_birth>'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\hline\n",
      "\\textbf{Harold Theobald}\n",
      " & harold & cricketer & united kingdom & male & 1896 03 18 \\\\\n",
      "\\hline\n",
      "\\hline\n",
      "{\\small in a minor counties record first-wicket partnership of 323 with | harold theobald | .} & harold & cricketer & united kingdom & male & 1912 01 01\\\\\n",
      "\\hline\n",
      "\\hline\n",
      "\\textbf{Dan Hardy}\n",
      " & dan & thai boxer & united kingdom & male & 1982 05 17 \\\\\n",
      "\\hline\n",
      "\\hline\n",
      "{\\small ludwig fought | dan hardy | on may 26 , 2012 , at ufc 146 .} & \\textcolor[HTML]{777777}{dan} & \\textcolor[HTML]{777777}{mixed martial artist} & \\textcolor[HTML]{777777}{united states of america} & male & 1985 01 01\\\\\n",
      "\\hline\n",
      "{\\small first mma events , the ksbo , where they met | dan hardy | and coach nathan leverton .} & \\textcolor[HTML]{777777}{dan} & \\textcolor[HTML]{777777}{association football player} & \\textcolor[HTML]{777777}{united states of america} & \\textcolor[HTML]{777777}{male} & \\textcolor[HTML]{777777}{1976 01 01}\\\\\n",
      "\\hline\n",
      "{\\small he was scheduled to fight | dan hardy | on november 14 , 2009 at ufc 105 , but} & \\textcolor[HTML]{777777}{dan} & mixed martial artist & \\textcolor[HTML]{777777}{united states of america} & \\textcolor[HTML]{777777}{male} & \\textcolor[HTML]{777777}{1983 01 01}\\\\\n",
      "\\hline\n",
      "{\\small fights before going on a three fight losing streak against | dan hardy | , brandon wolff and tj grant .} & \\textcolor[HTML]{777777}{dan} & \\textcolor[HTML]{777777}{mixed martial artist} & united states of america & \\textcolor[HTML]{777777}{male} & \\textcolor[HTML]{777777}{1983 01 01}\\\\\n",
      "\\hline\n",
      "{\\small in his post fight interview , he called out | dan hardy | , the \\# 1 contender in the welterweight division .} & dan & \\textcolor[HTML]{777777}{boxer} & \\textcolor[HTML]{777777}{united states of america} & \\textcolor[HTML]{777777}{male} & \\textcolor[HTML]{777777}{1978 01 01}\\\\\n",
      "\\hline\n",
      "\\hline\n",
      "\\textbf{Vladimir Ryzhenkov}\n",
      " & wladimir & weightlifter & russia & male & 1948 08 27 \\\\\n",
      "\\hline\n",
      "\\hline\n",
      "{\\small also in 1993 , | vladimir ryzhenkov | , who was at the time the belarus minister for} & vladimir & politician & russia & male & 1952 01 01\\\\\n",
      "\\hline\n",
      "\\hline\n",
      "\\textbf{Pamela Munro}\n",
      " & pamela & linguist & united states of america & female & 1947 05 23 \\\\\n",
      "\\hline\n",
      "\\hline\n",
      "{\\small among her students at ucsd were linguists | pamela munro | , leanne hinton , cheryl hinton , steve elster ,} & \\textcolor[HTML]{777777}{pamela} & \\textcolor[HTML]{777777}{pianist} & united states of america & \\textcolor[HTML]{777777}{female} & \\textcolor[HTML]{777777}{1952 01 01}\\\\\n",
      "\\hline\n",
      "{\\small a more recent and controversial classification has been proposed by | pamela munro | .} & \\textcolor[HTML]{777777}{pamela} & \\textcolor[HTML]{777777}{anthropologist} & \\textcolor[HTML]{777777}{united states of america} & \\textcolor[HTML]{777777}{male} & 1959 01 01\\\\\n",
      "\\hline\n",
      "{\\small by shaterian and an in process dictionary and grammar by | pamela munro | .} & \\textcolor[HTML]{777777}{pamela} & \\textcolor[HTML]{777777}{anthropologist} & \\textcolor[HTML]{777777}{united states of america} & male & \\textcolor[HTML]{777777}{1941 01 01}\\\\\n",
      "\\hline\n",
      "{\\small to the other languages is uncertain ; mary haas and | pamela munro | both classify it with the alabama–koasati group .} & pamela & \\textcolor[HTML]{777777}{philosopher} & \\textcolor[HTML]{777777}{united states of america} & \\textcolor[HTML]{777777}{male} & \\textcolor[HTML]{777777}{1952 01 01}\\\\\n",
      "\\hline\n",
      "{\\small in 2000 , linguist | pamela munro | argued that nicoleño was part of the cupan subgroup of} & \\textcolor[HTML]{777777}{pamela} & linguist & \\textcolor[HTML]{777777}{united states of america} & \\textcolor[HTML]{777777}{male} & \\textcolor[HTML]{777777}{1947 01 01}\\\\\n",
      "\\hline\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "exclude = {\n",
    "    'Ron Finley (American football)',\n",
    "    'Roger Sherman',\n",
    "    'Alison Smale',\n",
    "    'Abigail Mejia',\n",
    "    'Chris Bingham',\n",
    "    'Eudoxia Lopukhina',\n",
    "    'Mary Dickens',\n",
    "    'Thomas W. Lawson (businessman)',\n",
    "    'Rosalind Brewer',\n",
    "    'Savanna Samson',\n",
    "    'Alex Hicks',\n",
    "    'Austin Codrington',\n",
    "    'Horatio Earle'\n",
    "}\n",
    "count = 0\n",
    "male_count = 0\n",
    "gender_counts = Counter()\n",
    "citizenship_counts = Counter()\n",
    "for entity_id, instance in islice(instances.items(), 200):\n",
    "    sources = instance['sources']\n",
    "    target_relations = instance['targets']\n",
    "\n",
    "    decodes_by_rsi = {\n",
    "        r:{\n",
    "            si: [d for d in instance['decodes'][r] if d['source_id'] == si]\n",
    "            for si in range(len(sources))\n",
    "        } for r in rels\n",
    "    }\n",
    "    decode_ranks_by_rsi = {r:{\n",
    "            si: instance['decodes'][r].index(decodes_by_rsi[r][si][0])\n",
    "            for si in range(len(sources))\n",
    "        } for r in rels\n",
    "    }\n",
    "    si_ranks_by_r = {\n",
    "        r:\n",
    "        sorted(range(len(sources)), key=lambda si: instance['decodes'][r].index(decodes_by_rsi[r][si][0]))\n",
    "        for r in rels\n",
    "    }\n",
    "    \n",
    "    male = decodes_by_rsi['<sex_or_gender>'][0][0]['tokens'][0] == 'male'\n",
    "    \n",
    "    if entity_id in exclude or any(format_source(s) is None for s in sources):\n",
    "        continue\n",
    "    if gender_counts['male'] >= 3 and male:\n",
    "        continue\n",
    "    if citizenship_counts[target_relations.get('<country_of_citizenship>')] == 1 and target_relations['<country_of_citizenship>'] == 'united states of america':\n",
    "        continue\n",
    "    if count >= 4:\n",
    "        break\n",
    "    count += 1\n",
    "    gender_counts[target_relations['<sex_or_gender>']] += 1\n",
    "    citizenship_counts[target_relations.get('<country_of_citizenship>')] += 1\n",
    "    \n",
    "    print('\\\\hline')\n",
    "    print('\\\\textbf{'+ entity_id + '}')\n",
    "    row = ''\n",
    "    for r in rels:\n",
    "        row += ' & ' + target_relations.get(r, '\\\\texttt{nil}')\n",
    "    row += ' \\\\\\\\'\n",
    "    print(row)\n",
    "    print('\\\\hline')\n",
    "    print('\\\\hline')\n",
    "\n",
    "    \n",
    "    \n",
    "    for si, s in enumerate(sources):\n",
    "        result = format_source(s)\n",
    "        \n",
    "        for r in rels:\n",
    "            #target = target_relations.get(relation)=\n",
    "            \n",
    "            decodes = decodes_by_rsi[r][si]\n",
    "            rank = si_ranks_by_r[r].index(si)\n",
    "            \n",
    "            system = ' '.join(takewhile(lambda t: t != '</s>', decodes[0]['tokens']))\n",
    "            if rank != 0:\n",
    "                system = '\\\\textcolor[HTML]{777777}{' + system + '}'\n",
    "\n",
    "            result += ' & ' + system # + ':' + str(si_ranks_by_r[r].index(si))\n",
    "        result = result.replace('#', '\\\\#')\n",
    "        result += '\\\\\\\\'\n",
    "        print(result)\n",
    "        print('\\\\hline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========================================================================\n",
      "                      Relation | base    sys@1   sys@2   sys@5   sys@10  \n",
      "=========================================================================\n",
      "               <date_of_death> | 0.0     0.0     0.0     0.2     0.2     \n",
      "               <date_of_birth> | 0.6     0.2     0.8     1.5     1.5     \n",
      "              <place_of_birth> | 2.1     6.5     9.6     15.9    15.9    \n",
      "               <sex_or_gender> | 82.5    90.1    95.9    96.4    96.4    \n",
      "      <country_of_citizenship> | 30.4    58.9    67.7    78.5    78.5    \n",
      "              <place_of_death> | 3.4     14.3    21.1    28.7    28.7    \n",
      "                  <given_name> | 2.6     81.0    84.2    85.9    85.9    \n",
      "                  <occupation> | 12.5    46.7    56.9    70.7    70.7    \n",
      "                 <educated_at> | 2.8     12.2    16.4    22.5    22.5    \n",
      "              <participant_of> | 7.2     20.3    24.6    31.9    31.9    \n",
      "                       <sport> | 46.1    60.2    64.8    81.2    81.2    \n",
      "       <member_of_sports_team> | 0.0     7.3     11.0    14.7    14.7    \n",
      "   <member_of_political_party> | 17.9    44.9    62.8    70.5    70.5    \n",
      "               <position_held> | 6.9     35.3    44.1    53.9    53.9    \n",
      "              <award_received> | 7.3     23.6    25.2    29.3    29.3    \n",
      "                 <family_name> | 2.1     0.0     0.0     0.0     0.0     \n",
      "_________________________________________________________________________\n",
      "                         Micro | 20.1    41.7    46.7    52.0    52.0    \n",
      "                         Macro | 14.0    31.3    36.6    42.6    42.6    \n"
     ]
    }
   ],
   "source": [
    "print_evaluation(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trim_items_data(path, filter_rels=None):\n",
    "    with open(os.path.join(path, 'output.trimmed.jsonl'), 'w') as out:\n",
    "        with gzip.open(os.path.join(path, 'output.jsonl.gz'), 'rt', encoding='utf-8') as f:\n",
    "            for line in tqdm(f):\n",
    "                obj = json.loads(line)\n",
    "                for k in obj['decode']['decodes']:\n",
    "                    obj['decode']['decodes'][k] = obj['decode']['decodes'][k][:1]\n",
    "                out.write(json.dumps(obj)+'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def iter_items(path, filter_rels=None):\n",
    "    with gzip.open(os.path.join(path, 'output.jsonl.gz'), 'rt', encoding='utf-8') as f:\n",
    "        for line in tqdm(f):\n",
    "            obj = json.loads(line)\n",
    "            for relation, target in obj['decode']['targets'].items():\n",
    "                if filter_rels is None or relation in filter_rels:\n",
    "                    system = obj['decode']['decodes'][relation][0]['decoded']\n",
    "                    score = obj['decode']['decodes'][relation][0]['score']\n",
    "                    source = obj['decode']['sources'][obj['decode']['decodes'][relation][0]['source_id']]\n",
    "                    yield obj['instance_id'], source, relation, target, system, score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance vs Mention Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_instance_sources(path, filter_rels=None):\n",
    "    inst_sources = {}\n",
    "    with gzip.open(os.path.join(path, 'output.jsonl.gz'), 'rt', encoding='utf-8') as f:\n",
    "        for line in tqdm(f):\n",
    "            obj = json.loads(line)\n",
    "            inst_sources[obj['instance_id']] = obj['decode']['sources']\n",
    "    return inst_sources\n",
    "\n",
    "instance_sources = get_instance_sources(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
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      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "subsampled_instance_source_counts = {}\n",
    "def iter_subsampled_items(path, subsample_size, filter_rels=None):\n",
    "    subsampled_instance_source_counts.clear()\n",
    "    with gzip.open(os.path.join(path, 'output.jsonl.gz'), 'rt', encoding='utf-8') as f:\n",
    "        for line in tqdm(f):\n",
    "            obj = json.loads(line)\n",
    "            if len(obj['decode']['sources']) == subsample_size:\n",
    "                retain_idxs = set(random.sample(range(0, subsample_size), random.randint(1, subsample_size)))\n",
    "            else:\n",
    "                continue\n",
    "            subsampled_instance_source_counts[obj['instance_id']] = len(retain_idxs)\n",
    "            for relation, target in obj['decode']['targets'].items():\n",
    "                if filter_rels is None or relation in filter_rels:\n",
    "                    decodes = [d for d in obj['decode']['decodes'][relation] if d['source_id'] in retain_idxs]\n",
    "                    system = decodes[0]['decoded']\n",
    "                    score = decodes[0]['score']\n",
    "                    source = obj['decode']['sources'][decodes[0]['source_id']]\n",
    "                    yield obj['instance_id'], source, relation, target, system, score\n",
    "subsampled_items = list(iter_subsampled_items(data_path, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4432552025887305, 0.4449040587406974, 0.44656767636224315)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_bootstrapped_ci(instances, interval, N):\n",
    "    scores = [numpy.mean(numpy.random.choice(instances, size=len(instances), replace=True)) for _ in range(N)]\n",
    "    score = numpy.mean(instances)\n",
    "    interval_slice = (100-interval)/2\n",
    "    lower, upper = numpy.percentile(scores, [interval_slice, 100-interval_slice])\n",
    "    return lower, score, upper\n",
    "\n",
    "get_bootstrapped_ci([1.0 if i[3] == i[4] else 0.0 for i in items], 99, 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "1c64bcb35899417db1e27569b2eeeb43",
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       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
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       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
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       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from itertools import cycle\n",
    "\n",
    "ALPHA = 95\n",
    "N_BOOTSTRAP_SAMPLES = 1000\n",
    "\n",
    "def get_performance_vs_source_count_df(items, instance_source_counts):\n",
    "    df = []\n",
    "    for count in tqdm(range(1, 5+1)):\n",
    "        items_at_count = [i for i in items if instance_source_counts[i[0]] == count]\n",
    "        for r in relation_order:\n",
    "            results = [1.0 if i[3] == i[4] else 0.0 for i in items_at_count if i[2] == r]\n",
    "            lower, score, upper = get_bootstrapped_ci(results, ALPHA, N_BOOTSTRAP_SAMPLES)\n",
    "            df.append({\n",
    "                'count': count,\n",
    "                'fact': r,\n",
    "                'num_items': len(results),\n",
    "                'lower_bound': lower,\n",
    "                'score': score,\n",
    "                'upper_bound': upper,\n",
    "            })\n",
    "    return pd.DataFrame(df)\n",
    "\n",
    "all_entities_df = get_performance_vs_source_count_df(items, {k:len(vs) for k, vs in instance_sources.items()})\n",
    "subsampled_entities_df = get_performance_vs_source_count_df(subsampled_items, subsampled_instance_source_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scores_vs_link_count_by_fact_type(df, legend=True):\n",
    "    fig = plt.figure(figsize=(8,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    \n",
    "    lines = [\"-\",\"-.\"]\n",
    "    linecycler = cycle(lines)\n",
    "\n",
    "    relation_order_by_perf = list(df[df['count'] == 5].sort_values('score')['fact'])\n",
    "    relation_order_by_perf.reverse()\n",
    "    \n",
    "    for key, group in sorted(list(df.groupby('fact')), key=lambda kv: relation_order_by_perf.index(kv[0])):\n",
    "        name = key.replace('<','').replace('>','').replace('_', ' ')\n",
    "        name = relation_name_map.get(name, name)\n",
    "        #ax.plot(group['count'], group['score'], label=name)\n",
    "        \n",
    "        (_, caps, _) = ax.errorbar(\n",
    "            x=group['count'],\n",
    "            y=group['score'],\n",
    "            yerr=[group['score']-group['lower_bound'], group['upper_bound']-group['score']],\n",
    "            label=name,\n",
    "            capsize=5,\n",
    "            fmt='-o')\n",
    "        for cap in caps:\n",
    "            cap.set_markeredgewidth(1)\n",
    "\n",
    "    ax.set_xlim(1, 5)\n",
    "    ax.set_ylim(0, 1)\n",
    "    #plt.ylabel('Precision', fontsize='xx-large')\n",
    "    #plt.xlabel('Inlink Count', fontsize='xx-large')\n",
    "    plt.xticks([i for i in range(1, 5+1)], fontsize='large')\n",
    "    plt.yticks([i/10 for i in range(0, 11)], fontsize='large')\n",
    "    if legend:\n",
    "        plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0, fontsize='x-large',frameon=False)\n",
    "    plt.show()\n",
    "\n",
    "plot_scores_vs_link_count_by_fact_type(all_entities_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ad931239e4c43cfbb21528bc32b5213",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9afc141540c04a12960d74d539700a61",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_macro_scores_vs_link_count(dfs, legend=True):\n",
    "    fig = plt.figure(figsize=(8,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    \n",
    "    lines = [\"-\",\"-.\"]\n",
    "    linecycler = cycle(lines)\n",
    "    \n",
    "    for k, df in dfs:\n",
    "        group = df.groupby('count').mean()\n",
    "        #ax.plot(group.index, group['score'], label=k)\n",
    "        \n",
    "        (_, caps, _) = ax.errorbar(\n",
    "            x=group.index,\n",
    "            y=group['score'],\n",
    "            yerr=[group['score'] - group['lower_bound'], group['upper_bound']-group['score']],\n",
    "            label=k,\n",
    "            capsize=5,\n",
    "            fmt='-o')\n",
    "\n",
    "        for cap in caps:\n",
    "            cap.set_markeredgewidth(1)\n",
    "\n",
    "        ax.set_xlim(0.9, 5.1)\n",
    "        ax.set_ylim(0.2, 0.4)\n",
    "\n",
    "    #plt.ylabel('Macro Precision', fontsize='xx-large')\n",
    "    #plt.xlabel('Inlink Count', fontsize='xx-large')\n",
    "    plt.xticks([i for i in range(1, 5+1)], fontsize='large')\n",
    "    plt.yticks([i/20 for i in range(4, 10)], fontsize='large')\n",
    "    if legend:\n",
    "        plt.legend(fontsize='xx-large')\n",
    "    plt.show()\n",
    "\n",
    "plot_macro_scores_vs_link_count([\n",
    "    ('All entities', all_entities_df),\n",
    "    ('5 or more inlinks',  subsampled_entities_df)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = evaluate_decodes(iter_entity_decodes(os.path.join(data_path, 'output.jsonl.gz')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.247980205475768, 3.0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.mean([len(vs) for vs in instance_sources.values()]), numpy.median([len(vs) for vs in instance_sources.values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d4d83d01949473aa34fb4c373325e00",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "items = list(iter_items(data_path))\n",
    "random.seed(1447)\n",
    "random.shuffle(items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_prfs_across_thresholds(items_iter):\n",
    "    scores = defaultdict(list)\n",
    "    items_by_r = defaultdict(list)\n",
    "    for item in items_iter:\n",
    "        iid, source, relation, target, system, score = item\n",
    "        items_by_r[relation].append(item)\n",
    "        scores[relation].append(score)\n",
    "\n",
    "    metrics = []\n",
    "    percentiles = [p/2 for p in list(range(0, 100*2, 1))]\n",
    "    for r in relation_order:\n",
    "        for percentile, threshold in zip(percentiles, numpy.percentile(scores[r], percentiles)):\n",
    "            tps = 0.\n",
    "            fps = 0.\n",
    "            fns = 0.\n",
    "            for iid, source, relation, target, system, score in items_by_r[r]:\n",
    "                if score >= threshold:\n",
    "                    tps += 1.0 if target == system else 0.0\n",
    "                    fps += 1.0 if target != system else 0.0\n",
    "                else:\n",
    "                    fns += 1.0\n",
    "\n",
    "            P = 0. if tps == 0 else tps / (tps+fps)\n",
    "            R = 0. if tps == 0 else tps / (tps+fns)\n",
    "            F = 0. if tps == 0 else 2 * P * R / (P+R)\n",
    "            metrics.append({\n",
    "                'fact': r,\n",
    "                'percentile': percentile,\n",
    "                'threshold': threshold,\n",
    "                'P': P,\n",
    "                'R': R,\n",
    "                'F': F\n",
    "            })\n",
    "\n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from itertools import cycle\n",
    "\n",
    "def plot_pvsr_across_fact_types(df, legend=True):\n",
    "    relation_order_by_perf = list(df[df['percentile'] == 0].sort_values('P')['fact'])\n",
    "    relation_order_by_perf.reverse()\n",
    "    fig = plt.figure(figsize=(7,7))\n",
    "    ax = fig.add_subplot(111)\n",
    "    #cm = plt.get_cmap('jet')\n",
    "    #ax.set_color_cycle([cm( 1.*relation_order_by_perf.index(relation_order[i]) /len(relation_order)) for i in range(len(relation_order))])\n",
    "\n",
    "    lines = [\"-\",\"-.\"]\n",
    "    linecycler = cycle(lines)\n",
    "\n",
    "    for key, group in sorted(list(df.groupby('fact')), key=lambda kv: relation_order_by_perf.index(kv[0])):\n",
    "        group = group.sort_values('percentile')\n",
    "        name = key.replace('<','').replace('>','').replace('_', ' ')\n",
    "        name = relation_name_map.get(name, name)\n",
    "        ax.plot(group['R'], group['P'], label=name) #.plot(x='R', y='P',figsize=(16,8))\n",
    "    ax.set_xlim(0, 1)\n",
    "    ax.set_ylim(0, 1)\n",
    "    #plt.ylabel('Precision', fontsize='xx-large')\n",
    "    #plt.xlabel('Recall', fontsize='xx-large')\n",
    "    plt.xticks([i/10 for i in range(0, 11)], fontsize='large')\n",
    "    plt.yticks([i/10 for i in range(0, 11)], fontsize='large')\n",
    "    if legend:\n",
    "        plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0, fontsize='x-large',frameon=False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ac160850a1c4b9bb21543b3bc2b9bc6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97c983ed95704232aa6f25408a797914",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#lnk_metrics_df = pd.DataFrame(get_prfs_across_thresholds(islice(iter_items('../data/results/mentions'), None)))\n",
    "#bio_metrics_df = pd.DataFrame(get_prfs_across_thresholds(islice(iter_items('../data/results/bio'), None)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pvsr_across_fact_types(lnk_metrics_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pvsr_across_fact_types(bio_metrics_df, legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "ax = fig.add_subplot(111)\n",
    "#cm = plt.get_cmap('jet')\n",
    "#ax.set_color_cycle([cm( 1.*relation_order_by_perf.index(relation_order[i]) /len(relation_order)) for i in range(len(relation_order))])\n",
    "\n",
    "\n",
    "group = bio_metrics_df.groupby('percentile').mean()\n",
    "ax.plot(group['R'], group['P'], 'o', label='BIO model')\n",
    "\n",
    "group = lnk_metrics_df.groupby('percentile').mean()\n",
    "ax.plot(group['R'], group['P'], 'o', label='LNK model')\n",
    "\n",
    "\n",
    "ax.set_xlim(0, 1)\n",
    "ax.set_ylim(0, 1)\n",
    "#plt.ylabel('Precision', fontsize='xx-large')\n",
    "#plt.xlabel('Recall', fontsize='xx-large')\n",
    "plt.xticks([i/10 for i in range(0, 11)], fontsize='large')\n",
    "plt.yticks([i/10 for i in range(0, 11)], fontsize='large')\n",
    "plt.legend(loc=0, fontsize='xx-large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Pope Anianus of Alexandria',\n",
       " 'in 1288 the relics of saint | anianus | - the first successors of st . mark as patriarch of alexandria - were brought to the san clemente church .',\n",
       " '<country_of_citizenship>',\n",
       " 'egypt',\n",
       " 'italy',\n",
       " -1.058839798)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Date of birth Inference Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7794332946062653, 0.0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_years = []\n",
    "system_years = []\n",
    "for entity, source, relation, target, system, score in items:\n",
    "    if relation == '<date_of_birth>' and system.split()[0].isdigit() and target.split()[0].isdigit():\n",
    "        target_years.append(int(target.split()[0]))\n",
    "        system_years.append(int(system.split()[0]))\n",
    "from scipy.stats import pearsonr\n",
    "pearsonr(target_years, system_years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "numpy.median(numpy.abs(numpy.array(target_years) - numpy.array(system_years)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explicitness Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "from ipywidgets import Layout\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "items = list(iter_items(data_path, relation_order[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Pending', 1)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NUM_SAMPLES = 250\n",
    "MAX_SAMPLES_PER_RELATION = NUM_SAMPLES/5\n",
    "\n",
    "def iter_samples(samples, max_per_relation, exclude_ids, relation_counts):\n",
    "    for entity, source, relation, target, system, score in samples:\n",
    "        if relation_counts[relation] >= max_per_relation:\n",
    "            continue\n",
    "        iid = entity + ':' + relation\n",
    "        if iid not in exclude_ids:\n",
    "            yield {\n",
    "                'id': iid,\n",
    "                'entity': entity,\n",
    "                'relation': relation,\n",
    "                'source': source,\n",
    "                'target': target,\n",
    "            }\n",
    "            relation_counts[relation] += 1\n",
    "\n",
    "annotations_path = os.path.join(data_path, 'annotations.jsonl')\n",
    "annotated_item_ids = set()\n",
    "relation_counts = Counter()\n",
    "with open(annotations_path, 'r') as f:\n",
    "    for line in f:\n",
    "        annotation = json.loads(line)\n",
    "        annotated_item_ids.add(annotation['item']['id'])\n",
    "        if annotation['annotation']['decision'] != 'error':\n",
    "            relation_counts[annotation['item']['relation']] += 1\n",
    "\n",
    "pending = list(iter_samples(items, MAX_SAMPLES_PER_RELATION, annotated_item_ids, relation_counts))\n",
    "state = {\n",
    "    'item': None\n",
    "}\n",
    "\n",
    "'Pending', len(pending)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55bc3984db534b1782ce8b7961b95062",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "VBox(children=(HTML(value=''), HTML(value='', layout=Layout(width='50%')), HTML(value='', layout=Layout(width='50%'))), layout=Layout(width='100%'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4fc809c5acf7480da5172ad0244ba6ec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Checkbox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Checkbox(value=False, description='Interesting?')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "304bcd0680434e469ffd15879450a044",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>ToggleButtons</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "ToggleButtons(description='Inference:', options=('Explicit', 'Justified', 'Guessable', 'Not Justified', 'Error'), tooltips=['', '', ''], value='Explicit')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "898530aba81647068d2c4a8bbb120e95",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Progress:', max=278), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text_source = widgets.HTML()\n",
    "text_target = widgets.HTML(layout=Layout(width='50%'))\n",
    "text_relation = widgets.HTML(layout=Layout(width='50%'))\n",
    "chk_interesting = widgets.Checkbox(value=False, description='Interesting?', disabled=False)\n",
    "\n",
    "btn_decision = widgets.ToggleButtons(\n",
    "    options=['Explicit', 'Justified', 'Guessable', 'Not Justified', 'Error'],\n",
    "    description='Inference:',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltips=['', '', ''],\n",
    ")\n",
    "\n",
    "progress_text = widgets.HTML()\n",
    "progress_bar = widgets.IntProgress(\n",
    "    min=0,\n",
    "    max=len(annotated_item_ids)+len(pending),\n",
    "    step=1,\n",
    "    description='Progress:',\n",
    "    bar_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
    "    orientation='horizontal'\n",
    ")\n",
    "\n",
    "display(widgets.VBox([\n",
    "    text_source,\n",
    "    text_relation,\n",
    "    text_target\n",
    "], layout=Layout(width='100%')))\n",
    "display(chk_interesting)\n",
    "display(btn_decision)\n",
    "display(widgets.HBox([progress_bar, progress_text]))\n",
    "\n",
    "def update():\n",
    "    item = state['item']\n",
    "    \n",
    "    chk_interesting.value = False\n",
    "    btn_decision.value = None\n",
    "    if item:\n",
    "        text_source.value = \"\"\"\n",
    "            <div style='margin:10px; padding: 20px;background: #eee'>\"\"\" + item['source']  + \"\"\"</div>\n",
    "        \"\"\"\n",
    "        rel_fmt = item['relation'].replace('<', '').replace('>', '').replace('_', ' ').title()\n",
    "        text_relation.value = \"\"\"\n",
    "            <b>\"\"\" + rel_fmt + \"\"\"</b>: \\\"\"\"\" + item['target'] + \"\"\"\\\"\n",
    "        \"\"\"\n",
    "    else:\n",
    "        text_source.value = 'Nothing left to annotated!'\n",
    "        text_relation.value=text_target.value=''\n",
    "\n",
    "    num_completed = len(annotated_item_ids)\n",
    "    progress_text.value = '[%d / %d] ' % (num_completed, num_completed+len(pending))\n",
    "    progress_bar.value = num_completed\n",
    "\n",
    "def get_next_item(state):\n",
    "    if pending:\n",
    "        return pending[0]\n",
    "\n",
    "def commit():\n",
    "    with open(annotations_path, 'a+') as f:\n",
    "        f.write(json.dumps({\n",
    "            'time': time(),\n",
    "            'item': state['item'],\n",
    "            'annotation': {\n",
    "                'interesting': chk_interesting.value,\n",
    "                'decision': btn_decision.value.lower()\n",
    "            }\n",
    "        }) + '\\n')\n",
    "        annotated_item_ids.add(state['item']['id'])\n",
    "\n",
    "    pending.pop(0)\n",
    "\n",
    "def transition():\n",
    "    state['item'] = get_next_item(state)\n",
    "    if not state['item']:\n",
    "        btn_decision.disabled=True\n",
    "    update()\n",
    "    \n",
    "def on_decision(b):\n",
    "    if btn_decision.value != None and state['item']:\n",
    "        commit()\n",
    "        transition()\n",
    "\n",
    "btn_decision.observe(on_decision, names='value')\n",
    "transition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Fact Type &        Explicit &      Reasonable &       Guessable &     Unjustified \\\\\n",
      "\\hline\\hline\n",
      "           \\texttt{given name} &            93.9 &             0.0 &             0.0 &             6.1 \\\\\n",
      "           \\texttt{occupation} &             2.2 &            46.7 &            35.6 &            15.6 \\\\\n",
      "          \\texttt{citizenship} &             6.7 &             6.7 &            20.0 &            66.7 \\\\\n",
      "        \\texttt{sex or gender} &             4.4 &            84.4 &             4.4 &             6.7 \\\\\n",
      "        \\texttt{date of birth} &             6.5 &             0.0 &             0.0 &            93.5 \\\\\n",
      "\\hline\n",
      "                     All Types &            23.9 &            27.0 &            11.7 &            37.4 \\\\\n"
     ]
    }
   ],
   "source": [
    "category_map = {\n",
    "    'explicit': 'explicit',\n",
    "    'justified': 'reasonable',\n",
    "    'guessable': 'guessable',\n",
    "    'not justified': 'unjustified',\n",
    "    'error': None\n",
    "}\n",
    "\n",
    "relation_counts = Counter()\n",
    "stats = defaultdict(Counter)\n",
    "with open(annotations_path, 'r') as f:\n",
    "    for line in f:\n",
    "        annotation = json.loads(line)\n",
    "        relation_counts[annotation['item']['relation']] += 1\n",
    "        if relation_counts[annotation['item']['relation']] <= MAX_SAMPLES_PER_RELATION:\n",
    "            decision = category_map[annotation['annotation']['decision']]\n",
    "            if decision is not None:\n",
    "                stats[annotation['item']['relation']][decision] += 1\n",
    "\n",
    "report_catgories = sorted(set(v for v in category_map.values() if v is not None), key=lambda c: list(category_map.values()).index(c))\n",
    "print('Fact Type'.rjust(30) + ' & ' + ' & '.join((r.title() + '').rjust(15) for r in report_catgories), '\\\\\\\\')\n",
    "print('\\\\hline\\\\hline')\n",
    "category_agg = defaultdict(int)\n",
    "category_totals = defaultdict(int)\n",
    "\n",
    "for relation, counts in stats.items():\n",
    "    report = relation.replace('<', '').replace('>', '').replace('_', ' ')\n",
    "    report = report.replace('country of ', '')\n",
    "    report = '\\\\texttt{' + report + '}'\n",
    "    report = report.rjust(30)\n",
    "    for c in report_catgories:\n",
    "        category_agg[c] += counts[c]\n",
    "        category_totals[c] += sum(counts.values())\n",
    "        report += ' & ' + ('%.1f' % (counts[c]*100 / sum(counts.values()))).rjust(15)\n",
    "    print(report + ' \\\\\\\\')\n",
    "print('\\\\hline')\n",
    "print('All Types'.rjust(30) + ' & ' + ' & '.join(\n",
    "    ('%.1f'%(category_agg[c]*100/category_totals[c])).rjust(15) for c in report_catgories) + ' \\\\\\\\')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data_path' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-2143259cfa22>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mgzip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'output.jsonl.gz'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mislice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data_path' is not defined"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "items = []\n",
    "with gzip.open(os.path.join(data_path, 'output.jsonl.gz'), 'rt', encoding='utf-8') as f:\n",
    "    for line in tqdm(islice(f, 1000)):\n",
    "        obj = json.loads(line)\n",
    "        source = random.choice(obj['decode']['sources'])\n",
    "        score = obj['decode']['decodes'][relation][0]['score']\n",
    "        for relation, target in obj['decode']['targets'].items():\n",
    "            system = ' '.join(takewhile(lambda t: t != '</s>', obj['decode']['decodes'][relation][0]['tokens']))\n",
    "            if relation_order.index(relation) < 5:\n",
    "                items.append((obj['instance_id'], source, relation, target, system, score))\n",
    "\n",
    "random.seed(1447)\n",
    "random.shuffle(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "items = sorted(items, key=lambda i: i[-1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Clemo',\n",
       "  'the name was coined by | clemo | and popularized by kenyan rappers jua cali and nonini who started off at calif records , and is commonly sung in sheng ( slang ) , swahili',\n",
       "  '<country_of_citizenship>',\n",
       "  'kenya',\n",
       "  'japan',\n",
       "  0.0),\n",
       " ('William M. Gardner',\n",
       "  'fortunately for augustus , his commanding officer , captain | william m. gardner | , testified on his behalf and prevailed on the court to suspend the harsher penalties .',\n",
       "  '<sex_or_gender>',\n",
       "  'male',\n",
       "  'male',\n",
       "  0.0),\n",
       " ('Shafik Wazzan',\n",
       "  'from 1980 to 1982 , he served in the cabinet as minister of public works under president elias sarkis and prime minister | shafik wazzan | .',\n",
       "  '<date_of_birth>',\n",
       "  '1925 01 01',\n",
       "  '1928 01 01',\n",
       "  0.0),\n",
       " ('Zbigniew Beta',\n",
       "  '| zbigniew beta | \" \"',\n",
       "  '<sex_or_gender>',\n",
       "  'male',\n",
       "  'male',\n",
       "  0.0),\n",
       " ('Georges Foucart',\n",
       "  'he was the father of egyptologist | georges foucart | .',\n",
       "  '<sex_or_gender>',\n",
       "  'male',\n",
       "  'male',\n",
       "  0.0),\n",
       " ('Stephen Thomas (rugby player)',\n",
       "  'wales : billy bancroft ( swansea ) , tom pearson ( cardiff ) , dickie garrett ( penarth ) , charlie thomas ( newport ) , percy lloyd ( llanelli ) , evan james (',\n",
       "  '<date_of_birth>',\n",
       "  '1865 08 22',\n",
       "  '1878 01 01',\n",
       "  0.0),\n",
       " ('Daniel Curtis Lee',\n",
       "  'throughout the series , ned is played by devon werkheiser , cookie by | daniel curtis lee | , and lindsey shaw as moze .',\n",
       "  '<country_of_citizenship>',\n",
       "  'united states of america',\n",
       "  'united states of america',\n",
       "  0.0),\n",
       " ('Ashikaga Yoshinori',\n",
       "  'kamakura was finally attacked by shogun | ashikaga yoshinori | and retaken by force .',\n",
       "  '<occupation>',\n",
       "  'samurai',\n",
       "  'politician',\n",
       "  0.0),\n",
       " ('José Carlos Somoza',\n",
       "  'soto , norberto luis romero , care santos , | josé carlos somoza | , josé maría tamparillas , david torres , josé miguel vilar-bou and marian womack .',\n",
       "  '<occupation>',\n",
       "  'psychiatrist',\n",
       "  'painter',\n",
       "  0.0),\n",
       " ('George Fenneman',\n",
       "  '| george fenneman | was the announcer , and basil adlam led the orchestra .',\n",
       "  '<occupation>',\n",
       "  'actor',\n",
       "  'radio personality',\n",
       "  0.0)]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t <sex_or_gender>\n",
      "\n",
      "fortunately for augustus , his commanding officer , captain | william m. gardner | , testified on his behalf and prevailed on the court to suspend the harsher penalties .\n",
      "\t male = male\n",
      "\n",
      "| zbigniew beta | \" \"\n",
      "\t male = male\n",
      "\n",
      "he was the father of egyptologist | georges foucart | .\n",
      "\t male = male\n",
      "\n",
      "prior to independence , he became minister of the interior , posts and telecommunications in 1976 , as part of the transitional government headed by | abdallah mohamed kamil | .\n",
      "\t male = male\n",
      "\n",
      "van hoolwerff , as helmsman on the dutch 8 metre \" hollandia \" , took the 2nd place with fellow crew members : | lambertus doedes | , henk kersken , cornelis van staveren , gerard\n",
      "\t male = male\n",
      "\n",
      "\t <date_of_birth>\n",
      "\n",
      "from 1980 to 1982 , he served in the cabinet as minister of public works under president elias sarkis and prime minister | shafik wazzan | .\n",
      "\t 1925 01 01 = 1928 01 01\n",
      "\n",
      "wales : billy bancroft ( swansea ) , tom pearson ( cardiff ) , dickie garrett ( penarth ) , charlie thomas ( newport ) , percy lloyd ( llanelli ) , evan james (\n",
      "\t 1865 08 22 = 1878 01 01\n",
      "\n",
      "captain boom is a filipino comic book character created by | mars ravelo | and illustrated by his son ric ravelo .\n",
      "\t 1916 10 09 = 1973 01 01\n",
      "\n",
      "douglas rain did the narration for the english version ; the french version was titled notre univers with narration by | gilles pelletier | .\n",
      "\t 1925 03 22 = 1959 01 01\n",
      "\n",
      "he was born in mt . morris , new york , younger brother to saxophonist pat labarbera , and trumpeter and arranger/composer | john labarbera | .\n",
      "\t 1945 11 10 = 1928 01 01\n",
      "\n",
      "\t <occupation>\n",
      "\n",
      "kamakura was finally attacked by shogun | ashikaga yoshinori | and retaken by force .\n",
      "\t samurai = politician\n",
      "\n",
      "soto , norberto luis romero , care santos , | josé carlos somoza | , josé maría tamparillas , david torres , josé miguel vilar-bou and marian womack .\n",
      "\t psychiatrist = painter\n",
      "\n",
      "| george fenneman | was the announcer , and basil adlam led the orchestra .\n",
      "\t actor = radio personality\n",
      "\n",
      "madani competed in men 's doubles at the 1979 us open where he partnered mike myburg but were beaten by | steve docherty | and john james in the first round .\n",
      "\t tennis player = tennis player\n",
      "\n",
      "in february 2016 , eight players signed as full-time players for the first time in the club 's history : emily simpkins , rhiannon roberts , | courtney sweetman-kirk | , natasha dowie , becky\n",
      "\t association football player = association football player\n",
      "\n",
      "\t <given_name>\n",
      "\n",
      "the current dean of carey business school is | bernard t. ferrari | .\n",
      "\t bernard = bernard\n",
      "\n",
      "brother of damien and | darren gaspar | .\n",
      "\t darren = darren\n",
      "\n",
      "professor of the moscow theological academy | a.i. osipov | analyzes the teaching on the prayer by st . ignatius ( brianchaninov ) and points out that the prayer should have three properties : attention\n",
      "\t alexei = constantin\n",
      "\n",
      "once again , the british government did not wish to entrust its interests in japan to a foreign officer , so the british chiefs of staff appointed air vice marshal | cecil bouchier | as\n",
      "\t cecil = cecil\n",
      "\n",
      "| michael faulds | of western mustangs was in sixth place just behind brannagan with 9,137 career passing yards and justin dunk , of the guelph gryphons was seventh with 9,093 passing yards .\n",
      "\t michael = michael\n",
      "\n",
      "\t <country_of_citizenship>\n",
      "\n",
      "the name was coined by | clemo | and popularized by kenyan rappers jua cali and nonini who started off at calif records , and is commonly sung in sheng ( slang ) , swahili\n",
      "\t kenya = japan\n",
      "\n",
      "throughout the series , ned is played by devon werkheiser , cookie by | daniel curtis lee | , and lindsey shaw as moze .\n",
      "\t united states of america = united states of america\n",
      "\n",
      "gohan is voiced in the original japanese anime and all other media by | masako nozawa | .\n",
      "\t japan = japan\n",
      "\n",
      "planned by | henric piccardt | ( 1636-1712 ) , lord of the manors of klein martijn at harkstede and the fraeylemaborg at slochteren , it was built by two master craftsmen from the city\n",
      "\t netherlands = germany\n",
      "\n",
      "polonora again led his team in scoring with a game-best 20 points , complemented by 15 points and 8 assists from andrea cinciarini whilst | jeff brooks | paced sassari with 18 points and 11\n",
      "\t united states of america = united states of america\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for r in relation_order[:5]:\n",
    "    print('\\t', r)\n",
    "    print()\n",
    "    ris = [i for i in items if i[2] == r]\n",
    "    for i in ris[:5]:\n",
    "        print(i[1])\n",
    "        print('\\t', i[3], '=', i[4])\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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